In the United States, the Food and Drug Administration (FDA) oversees the protection of consumers exposed to health-related products ranging from food, cosmetics, drugs, gene therapies, and medical devices. Under the FDA guidance, clinical trials are performed to test the safety and efficacy of new drugs, medical devices or other treatments to ultimately ascertain whether or not a new medical therapy is appropriate for widespread human consumption.
More specifically, once a new drug or medical device has undergone studies in animals, and results appear favorable, it can be studied in humans. Before human testing is begun, findings of animal studies are reported to the FDA to obtain approval to do so. This report to the FDA is called an application for an Investigational New Drug (IND).
The process of experimentation is referred to as a clinical trial, which involves four phases. In Phase I, a few research participants, referred to as subjects, (approximately 5 to 10) are used to determine toxicity of a new treatment. In Phase II, more subjects (10-20) are used to determine efficacy and further ascertain safety. Doses are stratified to try to gain information about the optimal portion. A treatment may be compared to either a placebo or another existing therapy. In Phase III, efficacy is determined. For this phase, more subjects on the order of hundreds to thousands of patients are needed to perform a meaningful statistical analysis. A treatment may be compared to either a placebo or another existing therapy. In Phase IV (post-approval study), the treatment has already been approved by the FDA, but more testing is performed to evaluate long-term effects and to evaluate other indications.
During clinical trials, patients are seen at medical clinics and asked to participate in a clinical research project by their doctor, known as an investigator. After the patients sign an informed consent form, they are considered enrolled in the study, and are subsequently referred to as study subjects. A study sponsor, generally considered to be the company developing a new medical treatment and supporting the research, develops a study protocol. The study protocol is a document describing the reason for the experiment, the rationale for the number of subjects required, the methods used to study the subjects, and any other guidelines or rules for how the study is to be conducted. Prior to usage, the study protocol is reviewed and approved by an Institutional Review Board (IRB). An IRB serves as a peer review group, which evaluates a protocol to determine its scientific soundness and ethics for the protection of the subjects and investigator.
Subjects enrolled in a clinical study are stratified into groups that allow data to be assessed in a comparative fashion. In a common example, one study arm, known as a control group (or “control”), will use a placebo, whereby a pill containing no active chemical ingredient is administered. In doing so, comparisons can be made between subjects receiving actual medication versus placebo.
Subjects enrolled into a clinical study are assigned to a study arm in a random fashion, which is done to avoid biases that may occur in the selection of subjects for a trial. For example, a subject who is a particularly good candidate to respond to a new medication might be intentionally entered into the study arm to receive real medication and not a placebo. This could skew the data and outcome of the clinical trial to favor the medication under study, by the selection of subjects who are most likely to perform well with the medication. In instances where only one study group is present, randomization is not performed.
Blinding is a process by which the study arm assignment for subjects in a clinical trial is not revealed to the subject (single blind) or to both the subject and the investigator (double blind). This minimizes the risk of data bias. Virtually all randomized trials are blinded by definition. In instances where only one study group is present, blinding is not performed.
Generally, at the end of the trial, the database containing the completed trial data is shipped to a statistician for analysis. If particular occurrences, such as adverse events, are seen with an incidence that is greater in one group over another such that it exceeds the likelihood of pure chance alone, then it can be stated that statistical significance has been reached. Using statistical calculations, the comparative incidence of any given occurrence between groups can be described by a numeric value, referred to as a “p-value”. A p-value of 1.0 indicates that there is a 100% likelihood that an incident occurred as the result of chance alone. Conversely, a p-value of 0.0 indicates that there is a 0% likelihood that an incident occurred as a result of chance alone. Generally, values of p<0.05 are considered to be “statistically significant”, and values of p<0.01 are considered “highly statistically significant”.
In some clinical trials, multiple study arms, or even a control group, may not be utilized. In such cases, only a single study group exists with all subjects receiving the same treatment. This is typically performed when historical data about the medical treatment, or a competing treatment is already known from prior clinical trials, and may be utilized for the purpose of making comparisons.
The creation of study arms, randomization, and blinding are techniques that are used in most clinical trials where scientific rigor is of high importance. However, these methods lead to several challenges, since they prevent the clinical trial sponsor from tracking key information related to safety and efficacy.
Regarding safety, the objective of any clinical trial is to document the safety of a new treatment. However, in clinical trials where randomization is conducted between two or more study arms, this can be determined only as a result of analyzing and comparing the safety parameters of one study group to another. Unfortunately, because the study arm assignments are blinded, there is no way to separate out subjects and their data into corresponding groups for purposes of performing comparisons while the trial is being conducted. Since many clinical trials may last for time periods extending for years, it is conceivable to have a treatment toxicity go unnoticed for prolonged periods without intervention.
Regarding efficacy, any clinical trial seeking to document efficacy will incorporate key variables that are followed during the course of the trial to draw the desired conclusion. In addition, studies will define certain outcomes, or endpoints, at which point a study subject is considered to have completed the protocol. These parameters, including both key variables and study endpoints, cannot be analyzed by comparison between study arms while the subjects are randomized and blinded. This poses potential problems in ethics and statistical analysis.
When new medications or other health-related treatments are of superior efficacy to anything else, it is ethical to allow usage of the treatment for those in imminent need, even prior to final government approval. Conversely, when available, it is considered unethical to withhold such treatments. For example, if a medication were to be identified that eradicated the Human Immunodeficiency Virus (HIV), it would be unethical to allow diseased patients to continue suffering and even die of the illness, while the medication was being clinically tested for purposes of government approval. Ideally, in such situations, identification of effective treatments should occur early in the project. Under these circumstances, non-treatment arms (i.e., those taking placebos) could be construed as unethical and should be eliminated. At present, when clinical trials are randomized and blinded, identification of a particularly effective treatment may not be realized until the entire clinical trial is completed.
Another related problem is statistical power. By definition, statistical power refers to the probability of a test appropriately rejecting the null hypothesis, or the chance of an experiment's outcome being the result of chance alone. Clinical research protocols are engineered to prove a certain hypothesis about a medical treatment's safety and efficacy, and disprove the null hypothesis. To do so, statistical power is required, which can be achieved by obtaining a large enough sample size of subjects in each study arm. When too few subjects are enrolled into the study arms, there is the risk of the study not accruing enough subjects to enable the null hypothesis to be rejected, and thus not reaching statistical significance. Because clinical trials that are randomized are blinded, the actual number of subjects distributed throughout study arms is not defined until the end of the project. Although this maintains data collection integrity, there are inherent inefficiencies in the system, regardless of the outcome.
In a case where the study data reaches statistical significance, as accrual of subjects continues, and data is received, an optimal time to close a clinical study would be at the very moment when statistical significance is achieved. While that moment may arrive earlier in the course of a clinical trial, there is no way of knowing this, and therefore time and money are lost. Moreover, study subjects are enrolled above and beyond what is needed to reach the goals of the study, thus placing human subjects under experimentation unnecessarily.
In a case where the study data nearly reaches statistical significance, while the study data falls short of statistical significance, there is reason to believe that this is due to a shortage of enrollment in the study. Frequently, to develop more supportive data, clinical trials will be extended. These “extension studies”, however, can only begin after a full closure of the parent study, frequently requiring months to years before starting again.
In a case where the study data does not reach statistical significance, there is no trend toward significance, and there is little chance of reaching the desired conclusion. In that case, an optimal time to close a study is as early as possible once the conclusion can be established that the treatment under investigation does not work, and study data has little chance of reaching statistical significance (i.e., it is futile). In randomized and blinded clinical trials, this conclusion is difficult to arrive at until data analysis can be conducted. In these situations, time and money are lost. Moreover, an excess of human subjects are placed under study unnecessarily.
To mitigate some of the risks related to the conduct of randomized and blinded clinical trials, a Data Safety Monitoring Board (DSMB) may be formed at the beginning of each protocol. In general, a DSMB is recommended for clinical trials that involve a potentially serious outcome (e.g., death, heart attack, etc.), are randomized and blinded, and extend for prolonged periods of time. In addition, a DSMB is required for trials that are sponsored by the United States government, namely, the National Institute of Health (NIH).
A DSMB generally consists of members who are domain experts in the field of study, such as physicians, as well as bio-statisticians. It is important that DSMB members be separate from personnel of the sponsor organization, and financial disclosure for all members is performed to minimize conflicts of interest. Prior to start of a clinical trial, standard operating procedures are established for the DSMB, including the frequency of meetings, initiation of interim analyses, conduct during interim analyses and criteria for discontinuation of the clinical trial. As it relates to the safety of study subjects, DSMB functions to examine trends of adverse occurrences rather than investigate specific reports, which are generally left to each IRB responsible for the activities of any given investigator. That is, DSMB receives only a snapshot data of a clinical trial and not a continuous analysis of trial data as with the present invention. Additionally, if dangerous conditions/events (e.g., deaths of study patients) are detected then the clinical trial must be suspended/interrupted to perform data analysis of the clinical trial. Further, DSMB cannot determine whether such dangerous conditions exist with the control group taking the placebo or the study group taking the drug under study without suspending the clinical trial. That is, the snapshot data is not sufficient for DSMB to determine the cause of the dangerous condition. Accordingly, DSMB's specificity and sensitivity of detecting dangerous condition is very low because it cannot determine whether the dangerous condition is related to the drug under study. Therefore the present invention proceeds upon the desirability of resolving this problem by increasing the sensitivity to such dangerous conditions by performing continuous data analysis without interrupting the clinical trial.
A typical method of collecting and analyzing patient data is illustrated in the flow chart shown in FIG. 1. Patient data or charts 10 from the clinical trial are collected manually in paper forms. Using a technology called Electronic Data Capture (EDC) or Remote Data Entry (RDE), a computer (not shown) displays a Case Report Form (CRF) to a clinical research coordinator (CRC) 12, typically a nurse or doctor. The CRC 12 then enters the patient data 10 through the computer display which is received in block 14 by an EDC system which executes all of the steps included in a box 11. The received data is stored in a clinical trial database 38 through a link 20 which can be an electronic link such as a telephone line or Internet link. In block 18, it is determined whether the data inputted by the CRC 12 is clean using one or more rules. The rules may be implemented by simple range checking scripts, or by an inference rule engine or deterministic rule engine in order to identify potential problems with the data.
In addition to the software programs, block 18 may also involve research personnel known as monitors or Clinical Research Associates (CRA) who travels to the various research sites to perform source document verification (SDV) whereby the data in the database 38 is reconciled against individual patient charts to the degree required in the protocol.
If it is determined that the data entered is not clean, then block 22 generates a query which is then sent over the link 20 to the CRC 12. The blocks 14, 18 and 22 are repeated until all of the subject data 10 are entered. This is an iterative process that continues until resolution of all queries in the database 38.
Once all data 10 are entered, block 24 determines whether the clinical trial is over. If no, then the EDC system continues to receive the patient trial data 10 through block 14 as the trial continues. If the trial is over, control passes to block 26 where the entire database is locked from any changes, deletions or insertions of the data in the database 38. In one embodiment, locking involves turning the database 38 into a “read-only” state.
In block 28, a blinding data from a blinding database is retrieved. A simplified example blinding database 40 is shown in FIG. 4. The blinding database 40 is a database table having two columns. The first column contains a patient subject ID (subject identifier) and the second column contains an associated study arm or group the patient belongs to. In the table 40, 13 subjects belong to Study Arm “A” and 12 subjects belong to Study Arm “B”. Because the database 40 is not associated with actual trial data, the table 40 by itself is relatively uninformative.
A simplified example trial database 38 is shown in FIG. 5. The embodiment shown is a database table containing two columns. The first column contains a patient subject ID and the second column is a database field called “Heart Attack” which specifies whether the subject had a heart attack. An entry of 0 means NO and entry of 1 means YES. As can be seen from the trial database 38, due to blinding of the subjects in the study groups, there is no way of knowing whether or not any discrepancy exists in the number of heart attacks seen in Group A versus B. Because the trial is randomized, without the blinding data 40, the table 38 by itself is relatively uninformative.
In block 28, an unblinded database is produced from the trial database 38 and the retrieved blinding database 40 in which the subject ID is used as a common key. The result of the unblinding process of block 28 is shown in FIG. 6 as the unblinded database 41. In the embodiment shown, one database table is produced. The table 41 contains subject identifiers, Study Arm of the subjects, and Heart Attack data of those subjects. As can be appreciated by a person of ordinary skill in the art, there is a direct traceability from study data and subject ID to Study Arm.
In block 30, statistical analysis is performed on the unblinded data 42 to find out the efficacy and safety of the completed clinical trial.
During the course of any given randomized and blinded clinical trial, an interim analysis may be conducted. An interim analysis may result from urging of the DSMB for cause, or be a pre-planned event as described in the study protocol.
Conducting an interim analysis involves a process where the available data is verified and cleaned. The clinical trial is typically interrupted or suspended to enable the available data to be verified and cleaned. The verification process generally involves a process by which trained personnel travel to the various research sites to reconcile submitted data against source documents, which generally implies the patient's chart, laboratory reports, radiographic readings, and others. The data cleaning process may involve a series of documented communications between the research site and a central data coordinating personnel to resolve inconsistencies or other conflicting data.
The refined database must then be sent to an impartial third party for statistical analysis. To conduct the analysis, the statistician must un-blind the clinical trial database by combining both the study data with the blinding key of which subjects are assigned to particular study arms. Since the clinical study is expected to continue beyond the interim analysis, the process of un-blinding must be conducted with great caution, so as not to reveal the blind status of subjects to any personnel involved in the execution of the clinical trial. Once a statistician has completed the interim analysis, a report is issued to the trial sponsor and DSMB.
Inclusive of the data cleaning, verification, un-blinding and statistical analysis processes, as well as the administrative resources for coordinating several groups of personnel for the un-blinding process, an interim analysis is often arduous, time-consuming and expensive.
In spite of the latest technological advancements made in the area of data collection through electronic systems, there is still a disadvantage in that it is very difficult to draw conclusions about a medical treatment while the data is being collected during the trial. This limitation stems primarily from the fact that statistical analysis cannot begin until the trial data has been fully cleaned and processed. At present, statistical analysis can only be conducted upon data in an “en bloc” fashion. This creates a situation where the ability to draw conclusions about a medical therapy inevitably lags behind the process of simply obtaining data in a database.
Regardless of how efficient the data collection process may be made through automation, the ability to acquire the information needed for critical decision-making is still suspended by the requirement to obtain a locked database in order for statistical work to advance.
Therefore, it is desirable to provide a method and system for conducting data analysis, i.e., statistical analysis, on the clinical data collected while the clinical trial is ongoing. This advantageously permits the present invention to identify positive or negative conditions/events/trends much more rapidly than possible with currently available systems and methods.
In the case of a randomized clinical trial where maintaining confidentiality is important, it is also desirable to provide a secure system in which the blinding information is integrated in such a way that the clinical trial data and blinding data are stored securely to prevent users from accessing the data and yet allow the execution of programs for performing statistical comparisons between study arms while the clinical trial is ongoing.